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Remaining useful life prediction for a cracked rotor system via moving feature fusion based deep learning approach.

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Title: Remaining useful life prediction for a cracked rotor system via moving feature fusion based deep learning approach.
Authors: Khan, Imdad Ullah1 (AUTHOR) imdad1700@my.swjtu.edu.cn, Hua, Chunrong1 (AUTHOR) hcrong@swjtu.cn, Li, Longbin2 (AUTHOR), Zhang, Longyi1 (AUTHOR) longyi@my.swjtu.edu.cn, Yang, Funing1 (AUTHOR) yfn@my.swjtu.edu.cn, Liu, Weiqun1 (AUTHOR) weichunliu@home.swjtu.edu.cn
Source: Measurement (02632241). Jan2025, Vol. 239, pN.PAG-N.PAG. 1p.
Database: Academic Search Ultimate
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DbLabel: Academic Search Ultimate
An: 180295891
AccessLevel: 2
PubType: Academic Journal
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=180295891
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.measurement.2024.115433
    Languages:
      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Titles:
      – TitleFull: Remaining useful life prediction for a cracked rotor system via moving feature fusion based deep learning approach.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Khan, Imdad Ullah
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            NameFull: Hua, Chunrong
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            NameFull: Li, Longbin
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            NameFull: Zhang, Longyi
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            NameFull: Yang, Funing
      – PersonEntity:
          Name:
            NameFull: Liu, Weiqun
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          Dates:
            – D: 15
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
          Identifiers:
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              Value: 239
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            – TitleFull: Measurement (02632241)
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